In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import os
import re
from imp import reload
import h5py
In [2]:
import deltascope as cranium
import deltascope.alignment as ut
In [3]:
gfap = ".\data\hss1ayot\GFAP\Prob"
at = ".\data\hss1ayot\AT\Prob"
root = ".\data\hss1ayot"
In [4]:
outdir = os.path.join(root,'Output-02-15-2019')
os.mkdir(outdir)
In [5]:
Dat = {}
for f in os.listdir(at):
    if 'h5' in f:
        num  = re.findall(r'\d+',f.split('.')[0])[-1]
        Dat[num] = os.path.join(at,f)
In [6]:
Dzrf = {}
for f in os.listdir(gfap):
    if 'h5' in f:
        num  = re.findall(r'\d+',f.split('.')[0])[-1]
        Dzrf[num] = os.path.join(gfap,f)
In [7]:
Dbat = {}
Dbzrf = {}

Data processing

In [8]:
klist = Dat.keys()
In [9]:
param = {
    'gthresh':0.5,
    'scale':[1,1,1],
    'microns':[0.16,0.16,0.21],
    'mthresh':0.5,
    'radius':10,
    'comp_order':[0,2,1],
    'fit_dim':['x','z'],
    'deg':2
}
In [10]:
%%time
for k in klist:
    if k not in list(Dbat.keys()):
        Dbat[k] = ut.preprocess(Dat[k],param)
        Dbzrf[k] = ut.preprocess(Dzrf[k],param,pca=Dbat[k].pcamed,mm=Dbat[k].mm,vertex=Dbat[k].vertex)
        #Dbcaax[k] = ut.preprocess(Dcaax[k],param,pca=Dbat[k].pcamed,mm=Dbat[k].mm,vertex=Dbat[k].vertex)
        print(k)
    else:
        print(k,'already processed')
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
010
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
012
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
014
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
016
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
02
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
03
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
06
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
07
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
08
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
09
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
10
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
11
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
12
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
13
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
14
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
16
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
2
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
3
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
5
C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))
7
Wall time: 13min 33s

Functions

In [11]:
def start(k):
    return(ut.start(k,Dbat,[Dbzrf],im=True))
def save_both(k,dfa,dfb):
    ut.save_both(k,dfa,dfb,outdir,'hss1ayot')
In [12]:
model = pd.DataFrame({'a':[],'b':[],'c':[]})
def save_model(k,mm,model):
    row = pd.Series({'a':mm[0],'b':mm[1],'c':mm[2]},name=k)
    model = model.append(row)
    return(model)
In [13]:
def fit_model(axi,df,mm=None):
    if mm == None:
        mm = np.polyfit(df.x,df.z,2)
    p = np.poly1d(mm)
    xrange = np.arange(np.min(df.x),np.max(df.x))
    axi.plot(xrange,p(xrange),c='m')
    return(mm)
In [14]:
def pick_pts(x1,z1,vx,vz,x2,z2):
    pts = pd.DataFrame({'x':[x1,vx,x2],'z':[z1,vz,z2]})
    return(pts)

3

In [15]:
k,df,Ldf,ax = start('3')
In [16]:
mm = fit_model(ax[0,1],df)
In [17]:
model = save_model(k,mm,model)
save_both(k,df,Ldf[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_3_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_3_hss1ayot.psi complete

11

In [18]:
k,df,Ldf,ax = start('11')
In [19]:
mm = fit_model(ax[0,1],df)
In [20]:
model = save_model(k,mm,model)
save_both(k,df,Ldf[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_11_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_11_hss1ayot.psi complete
In [21]:
klist
Out[21]:
dict_keys(['010', '012', '014', '016', '02', '03', '06', '07', '08', '09', '10', '11', '12', '13', '14', '16', '2', '3', '5', '7'])

010

In [22]:
k,df,Ldf,ax = start('010')
In [23]:
df1,Ldf1 = ut.zyswitch(df,Ldf)
ax = ut.make_graph([df1]+Ldf1)
In [24]:
mm = fit_model(ax[0,1],df1)
In [25]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_010_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_010_hss1ayot.psi complete

09

Discard; too little signal

In [26]:
k,df,Ldf,ax = start('09')

016

discard; limited signal

In [27]:
k,df,Ldf,ax = start('016')

02

In [28]:
k,df,Ldf,ax = start('02')
In [29]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [30]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf)
In [31]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_02_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_02_hss1ayot.psi complete
In [32]:
klist
Out[32]:
dict_keys(['010', '012', '014', '016', '02', '03', '06', '07', '08', '09', '10', '11', '12', '13', '14', '16', '2', '3', '5', '7'])

14

In [33]:
k,df,Ldf,ax = start('14')
In [34]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [35]:
pts = pick_pts(0,12,34,3,72,18)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[35]:
<matplotlib.collections.PathCollection at 0x21813d8e898>
In [36]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [37]:
df2,Ldf2,pts,ax = ut.check_pts(df1,Ldf1,'z')
In [38]:
pts.iloc[0].x = 41
pts.iloc[0].z = 15
pts.iloc[1].z = 7
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[38]:
x z
0 41.000000 15.0
1 -31.450704 7.0
In [39]:
df3,Ldf3,ax = ut.revise_pts(df1,Ldf1,'z',pts=pts)
In [40]:
pts = pick_pts(-30,10,5,-1,43,10)
ax[1,1].scatter(pts.x,pts.z,c='m',s=50)
Out[40]:
<matplotlib.collections.PathCollection at 0x2181547f860>
In [41]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [42]:
model = save_model(k,mm,model)
save_both(k,df4,Ldf4[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_14_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_14_hss1ayot.psi complete

10

In [43]:
k,df,Ldf,ax = start('10')
In [44]:
pts = pick_pts(-25,5,3,-5,38,6)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[44]:
<matplotlib.collections.PathCollection at 0x218155c7668>
In [45]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [46]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_10_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_10_hss1ayot.psi complete

014

In [47]:
k,df,Ldf,ax = start('014')
In [48]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [49]:
pts = pick_pts(0,10,35,2,69,10)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[49]:
<matplotlib.collections.PathCollection at 0x218150f2048>
In [50]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [51]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_014_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_014_hss1ayot.psi complete

5

In [52]:
k,df,Ldf,ax = start('5')
In [53]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [54]:
pts = pick_pts(0,16,36,1,81,7)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[54]:
<matplotlib.collections.PathCollection at 0x21815bb2a90>
In [55]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [56]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_5_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_5_hss1ayot.psi complete

07

In [57]:
k,df,Ldf,ax = start('07')
In [58]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [59]:
pts = pick_pts(0,15,30,6,64,16)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[59]:
<matplotlib.collections.PathCollection at 0x2181a7fe2b0>
In [60]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [61]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_07_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_07_hss1ayot.psi complete

12

In [62]:
k,df,Ldf,ax = start('12')
In [63]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [64]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [65]:
pts.iloc[0].z = 12
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[65]:
x z
0 0.00 12.00
1 78.72 15.33
In [66]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [67]:
df3,Ldf3 = ut.flip(df2,Ldf2)
ax = ut.make_graph([df3]+Ldf3)
In [68]:
pts = pick_pts(-78,12,-36,0,0,10)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[68]:
<matplotlib.collections.PathCollection at 0x2187eacf908>
In [69]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [70]:
model = save_model(k,mm,model)
save_both(k,df4,Ldf4[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_12_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_12_hss1ayot.psi complete

03

In [71]:
k,df,Ldf,ax = start('03')
In [72]:
mm = fit_model(ax[0,1],df)
In [73]:
pts = pick_pts(-35,11,3,-2,40,11)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[73]:
<matplotlib.collections.PathCollection at 0x2187ed9bc88>
In [74]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [75]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_03_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_03_hss1ayot.psi complete

16

In [76]:
k,df,Ldf,ax = start('16')
In [77]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [78]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf)
In [79]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_16_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_16_hss1ayot.psi complete

08

In [80]:
k,df,Ldf,ax = start('08')
In [81]:
mm = fit_model(ax[0,1],df)
In [82]:
model = save_model(k,mm,model)
save_both(k,df,Ldf[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_08_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_08_hss1ayot.psi complete

13

In [83]:
k,df,Ldf,ax = start('13')
In [84]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [85]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf)
In [86]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_13_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_13_hss1ayot.psi complete

2

In [87]:
k,df,Ldf,ax = start('2')
In [88]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [89]:
pts = pick_pts(0,18,45,2,90,15)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[89]:
<matplotlib.collections.PathCollection at 0x2187e9efc50>
In [90]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [91]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_2_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_2_hss1ayot.psi complete

7

In [92]:
k,df,Ldf,ax = start('7')
In [93]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [94]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [95]:
pts.iloc[0].z = 4
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[95]:
x z
0 75.2 4.00
1 0.0 11.13
In [96]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [97]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [98]:
pts = pick_pts(-34,7,4,-1,42,5)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[98]:
<matplotlib.collections.PathCollection at 0x21917835c18>
In [99]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [100]:
model = save_model(k,mm,model)
save_both(k,df4,Ldf4[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_7_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_7_hss1ayot.psi complete

012

In [101]:
k,df,Ldf,ax = start('012')
In [102]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [103]:
pts = pick_pts(0,18,44,2,94,19)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[103]:
<matplotlib.collections.PathCollection at 0x2187ed1edd8>
In [104]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [105]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])
Write to .\data\hss1ayot\Output-02-15-2019\AT_012_hss1ayot.psi complete
Write to .\data\hss1ayot\Output-02-15-2019\ZRF_012_hss1ayot.psi complete

06

In [106]:
k,df,Ldf,ax = start('06')

Discard not enough zrf signal

In [107]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
ax = ut.make_graph([df]+Ldf)
In [108]:
model
Out[108]:
a b c
3 0.005765 -7.518081e-17 5.468862e-15
11 0.010150 -7.641207e-17 2.161441e-15
010 0.000698 -6.484719e-02 -2.723168e-01
02 0.004502 -2.600388e-17 3.801242e-15
14 0.008271 6.825507e-17 3.076827e-15
10 0.010658 -1.757130e-16 -1.812101e-14
014 0.006723 2.684945e-16 1.025566e-15
5 0.006790 -2.503965e-16 2.813754e-15
07 0.009283 -2.090030e-16 1.176538e-17
12 0.007224 -2.721652e-16 4.792220e-15
03 0.009246 -3.833467e-17 1.025582e-15
16 0.006386 -9.000242e-17 9.792146e-16
08 0.007753 -1.034213e-16 -1.531882e-15
13 0.009048 -2.644279e-16 -4.188198e-15
2 0.007160 -2.204101e-16 -7.797574e-15
7 0.004848 -7.684438e-17 -9.270975e-17
012 0.007485 -1.492182e-17 4.138972e-15
In [109]:
model.to_csv(os.path.join(outdir,'model.csv'))
In [110]:
outdir
Out[110]:
'.\\data\\hss1ayot\\Output-02-15-2019'
In [111]:
modelout = pd.read_csv(os.path.join(outdir,'model.csv'))
In [112]:
modelout
Out[112]:
Unnamed: 0 a b c
0 3 0.005765 -7.518081e-17 5.468862e-15
1 11 0.010150 -7.641207e-17 2.161441e-15
2 10 0.000698 -6.484719e-02 -2.723168e-01
3 2 0.004502 -2.600388e-17 3.801242e-15
4 14 0.008271 6.825507e-17 3.076827e-15
5 10 0.010658 -1.757130e-16 -1.812101e-14
6 14 0.006723 2.684945e-16 1.025566e-15
7 5 0.006790 -2.503965e-16 2.813754e-15
8 7 0.009283 -2.090030e-16 1.176538e-17
9 12 0.007224 -2.721652e-16 4.792220e-15
10 3 0.009246 -3.833467e-17 1.025582e-15
11 16 0.006386 -9.000242e-17 9.792146e-16
12 8 0.007753 -1.034213e-16 -1.531882e-15
13 13 0.009048 -2.644279e-16 -4.188198e-15
14 2 0.007160 -2.204101e-16 -7.797574e-15
15 7 0.004848 -7.684438e-17 -9.270975e-17
16 12 0.007485 -1.492182e-17 4.138972e-15
In [113]:
model2 = pd.read_csv(os.path.join(outdir,'model.csv'),index_col='Unnamed: 0',dtype={'Unnamed: 0':str})
In [114]:
model2
Out[114]:
a b c
3 0.005765 -7.518081e-17 5.468862e-15
11 0.010150 -7.641207e-17 2.161441e-15
10 0.000698 -6.484719e-02 -2.723168e-01
2 0.004502 -2.600388e-17 3.801242e-15
14 0.008271 6.825507e-17 3.076827e-15
10 0.010658 -1.757130e-16 -1.812101e-14
14 0.006723 2.684945e-16 1.025566e-15
5 0.006790 -2.503965e-16 2.813754e-15
7 0.009283 -2.090030e-16 1.176538e-17
12 0.007224 -2.721652e-16 4.792220e-15
3 0.009246 -3.833467e-17 1.025582e-15
16 0.006386 -9.000242e-17 9.792146e-16
8 0.007753 -1.034213e-16 -1.531882e-15
13 0.009048 -2.644279e-16 -4.188198e-15
2 0.007160 -2.204101e-16 -7.797574e-15
7 0.004848 -7.684438e-17 -9.270975e-17
12 0.007485 -1.492182e-17 4.138972e-15